Transforming data into intelligent solutions with cutting-edge machine learning and AI technologies.
Get to know the person behind the code
I'm Bhargav Sri Sai Nama — a Full Stack Machine Learning Engineer and Data Scientist passionate about building intelligent systems that create real-world impact.
With a Master’s in Data Science from the University of Colorado Boulder and a B.Tech in Mechanical Engineering from IIT Tirupati, my work spans the full ML lifecycle — from data engineering and model development to deployment and monitoring.
Currently, I lead machine learning initiatives at MODE Civil Services, where I design and deploy production-grade AI solutions. My contributions range from building LLM-powered automation for CAD workflows to developing scalable NLP pipelines and CI/CD-enabled ML infrastructure. Previously, I worked on industrial computer vision systems at Texas A&M University, delivering real-time defect detection for manufacturing optimization.
My technical stack includes Python, PyTorch, TensorFlow, Hugging Face Transformers, Azure, and Docker — and I’ve applied these across 15+ projects including legal document analysis (RAG), AI hallucination detection, and large-scale statistical modeling.
Whether it's training state-of-the-art models, engineering robust data workflows, or shipping AI products end-to-end — I thrive at the intersection of data science and machine learning engineering.Let’s build the future with data-driven intelligence.
My professional journey and academic background
MODE Civil Services
Leading end-to-end ML solution development and deployment.
MODE Civil Services
Developed NLP solutions and led machine learning initiatives.
Texas A&M University
Optimized manufacturing processes using ML and computer vision.
Data Science
University of Colorado Boulder
Aug 2023 - May 2025
Mechanical Engineering
Indian Institute of Technology Tirupati
Jul 2019 - May 2023
Full-stack ML development capabilities across the entire software lifecycle
Showcasing full-stack ML solutions and data science applications
Chrome extension for AI hallucination detection using Wikipedia and DuckDuckGo APIs with BERT-based NLI model for fact verification. Reduced hallucinated responses by 40%.
Local RAG pipeline for clause retrieval with FAISS and SentenceTransformers, increasing accuracy by 10%. Features Streamlit UI and Airflow-based pipeline processing 5,000+ clauses from 1,000+ legal documents.
AI-powered teaching assistant using advanced NLP techniques to help students with coursework and answer questions intelligently.
Statistical analysis exploring the relationship between unemployment rates and crime statistics using data science techniques.
In-situ monitoring system for Wire Arc Additive Manufacturing using computer vision and ML for quality assurance.
Ready to collaborate or discuss opportunities?